Citation

Leelavathi, R. and Prabha Devi, D. and Kodieswari, A. and Khan, Arfat Ahmad and V, Suma and Kamal, Shahid and Ullah, Fasee (2026) A cloud enabled hybrid CNN transformer framework with attention mechanisms for scalable Alzheimer's disease staging from structural MRI. Neuroscience Informatics, 6 (3). p. 100283. ISSN 27725286

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Abstract

Alzheimer's disease (AD) is a neurodegenerative condition that causes cognitive impairment and structural brain changes, and early diagnosis is crucial for treatment. This paper proposes a cloud-based hybrid deep learning architecture for scalable Alzheimer's disease staging based on structural magnetic resonance imaging (MRI). The hybrid architecture leverages convolutional neural networks (CNNs), attention, and transformer-based modeling to capture both local and global spatial features in MRI images. CNN extracts multi-dimensional features, while the attention and transformer layers enrich context representation for more accurate staging of the disease. The framework was trained on a GPU-enabled computer using the PyTorch deep learning framework on a publicly available MRI dataset for Alzheimer's disease from the Kaggle website. The model was cross-validated on the OASIS dataset to assess its generalizability. The experimental findings showed that the proposed approach achieved an accuracy of 99.65% on the Kaggle dataset, which is better than several state-of-the-art deep learning models such as VGG16, ResNet variants, DenseNet121, EfficientNet-B0, and Vision Transformer. This approach also achieved an accuracy of 91.67% on the OASIS dataset, demonstrating the model's ability to generalize across neuroimaging datasets. To enable practical usage and accessibility, the trained model was also deployed in a cloud-based inference environment on Hugging Face Spaces, which allows MRI image upload and prediction from a web browser. The scalable and cloud-deployable design allows scalable medical image processing and integration with cloud-based diagnostic systems as well as telemedicine-based healthcare solutions. These findings demonstrate the potential for robust and accessible staging of Alzheimer's disease using the proposed framework in clinical settings.

Item Type:	Article
Uncontrolled Keywords:	Alzheimer's disease, Structural MRI
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Jun 2026 01:40
Last Modified:	30 Jun 2026 01:40
URII:	http://shdl.mmu.edu.my/id/eprint/16107

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